Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.4 KiB
Average record size in memory234.7 B

Variable types

Numeric7
Categorical4

Alerts

App Usage Time (min/day) is highly overall correlated with Battery Drain (mAh/day) and 4 other fieldsHigh correlation
Battery Drain (mAh/day) is highly overall correlated with App Usage Time (min/day) and 4 other fieldsHigh correlation
Data Usage (MB/day) is highly overall correlated with App Usage Time (min/day) and 4 other fieldsHigh correlation
Device Model is highly overall correlated with Operating SystemHigh correlation
Number of Apps Installed is highly overall correlated with App Usage Time (min/day) and 4 other fieldsHigh correlation
Operating System is highly overall correlated with Device ModelHigh correlation
Screen On Time (hours/day) is highly overall correlated with App Usage Time (min/day) and 4 other fieldsHigh correlation
User Behavior Class is highly overall correlated with App Usage Time (min/day) and 4 other fieldsHigh correlation
User ID is uniformly distributedUniform
User ID has unique valuesUnique

Reproduction

Analysis started2024-10-05 01:17:37.589737
Analysis finished2024-10-05 01:17:43.464743
Duration5.88 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

User ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.5
Minimum1
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:43.625548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.95
Q1175.75
median350.5
Q3525.25
95-th percentile665.05
Maximum700
Range699
Interquartile range (IQR)349.5

Descriptive statistics

Standard deviation202.21688
Coefficient of variation (CV)0.57693832
Kurtosis-1.2
Mean350.5
Median Absolute Deviation (MAD)175
Skewness0
Sum245350
Variance40891.667
MonotonicityStrictly increasing
2024-10-05T06:47:43.799579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
471 1
 
0.1%
463 1
 
0.1%
464 1
 
0.1%
465 1
 
0.1%
466 1
 
0.1%
467 1
 
0.1%
468 1
 
0.1%
469 1
 
0.1%
470 1
 
0.1%
Other values (690) 690
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
700 1
0.1%
699 1
0.1%
698 1
0.1%
697 1
0.1%
696 1
0.1%
695 1
0.1%
694 1
0.1%
693 1
0.1%
692 1
0.1%
691 1
0.1%

Device Model
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size42.1 KiB
Xiaomi Mi 11
146 
iPhone 12
146 
Google Pixel 5
142 
OnePlus 9
133 
Samsung Galaxy S21
133 

Length

Max length18
Median length14
Mean length12.35
Min length9

Characters and Unicode

Total characters8645
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoogle Pixel 5
2nd rowOnePlus 9
3rd rowXiaomi Mi 11
4th rowGoogle Pixel 5
5th rowiPhone 12

Common Values

ValueCountFrequency (%)
Xiaomi Mi 11 146
20.9%
iPhone 12 146
20.9%
Google Pixel 5 142
20.3%
OnePlus 9 133
19.0%
Samsung Galaxy S21 133
19.0%

Length

2024-10-05T06:47:44.262777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T06:47:44.395801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
xiaomi 146
 
8.0%
mi 146
 
8.0%
11 146
 
8.0%
iphone 146
 
8.0%
12 146
 
8.0%
google 142
 
7.8%
pixel 142
 
7.8%
5 142
 
7.8%
oneplus 133
 
7.3%
9 133
 
7.3%
Other values (3) 399
21.9%

Most occurring characters

ValueCountFrequency (%)
1121
 
13.0%
i 726
 
8.4%
o 576
 
6.7%
1 571
 
6.6%
e 563
 
6.5%
l 550
 
6.4%
a 545
 
6.3%
P 421
 
4.9%
n 412
 
4.8%
2 279
 
3.2%
Other values (14) 2881
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1121
 
13.0%
i 726
 
8.4%
o 576
 
6.7%
1 571
 
6.6%
e 563
 
6.5%
l 550
 
6.4%
a 545
 
6.3%
P 421
 
4.9%
n 412
 
4.8%
2 279
 
3.2%
Other values (14) 2881
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1121
 
13.0%
i 726
 
8.4%
o 576
 
6.7%
1 571
 
6.6%
e 563
 
6.5%
l 550
 
6.4%
a 545
 
6.3%
P 421
 
4.9%
n 412
 
4.8%
2 279
 
3.2%
Other values (14) 2881
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1121
 
13.0%
i 726
 
8.4%
o 576
 
6.7%
1 571
 
6.6%
e 563
 
6.5%
l 550
 
6.4%
a 545
 
6.3%
P 421
 
4.9%
n 412
 
4.8%
2 279
 
3.2%
Other values (14) 2881
33.3%

Operating System
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
Android
554 
iOS
146 

Length

Max length7
Median length7
Mean length6.1657143
Min length3

Characters and Unicode

Total characters4316
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndroid
2nd rowAndroid
3rd rowAndroid
4th rowAndroid
5th rowiOS

Common Values

ValueCountFrequency (%)
Android 554
79.1%
iOS 146
 
20.9%

Length

2024-10-05T06:47:44.618546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T06:47:44.739320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
android 554
79.1%
ios 146
 
20.9%

Most occurring characters

ValueCountFrequency (%)
d 1108
25.7%
i 700
16.2%
A 554
12.8%
n 554
12.8%
r 554
12.8%
o 554
12.8%
O 146
 
3.4%
S 146
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1108
25.7%
i 700
16.2%
A 554
12.8%
n 554
12.8%
r 554
12.8%
o 554
12.8%
O 146
 
3.4%
S 146
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1108
25.7%
i 700
16.2%
A 554
12.8%
n 554
12.8%
r 554
12.8%
o 554
12.8%
O 146
 
3.4%
S 146
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1108
25.7%
i 700
16.2%
A 554
12.8%
n 554
12.8%
r 554
12.8%
o 554
12.8%
O 146
 
3.4%
S 146
 
3.4%

App Usage Time (min/day)
Real number (ℝ)

HIGH CORRELATION 

Distinct387
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.12857
Minimum30
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:44.856180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile46
Q1113.25
median227.5
Q3434.25
95-th percentile565.05
Maximum598
Range568
Interquartile range (IQR)321

Descriptive statistics

Standard deviation177.19948
Coefficient of variation (CV)0.65356256
Kurtosis-1.2596179
Mean271.12857
Median Absolute Deviation (MAD)146
Skewness0.37231182
Sum189790
Variance31399.657
MonotonicityNot monotonic
2024-10-05T06:47:44.989401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 7
 
1.0%
78 5
 
0.7%
152 5
 
0.7%
138 5
 
0.7%
516 5
 
0.7%
225 5
 
0.7%
46 5
 
0.7%
105 5
 
0.7%
75 5
 
0.7%
66 5
 
0.7%
Other values (377) 648
92.6%
ValueCountFrequency (%)
30 3
0.4%
31 1
 
0.1%
32 3
0.4%
33 2
0.3%
34 3
0.4%
35 1
 
0.1%
36 2
0.3%
37 3
0.4%
39 3
0.4%
41 4
0.6%
ValueCountFrequency (%)
598 1
 
0.1%
597 2
0.3%
595 1
 
0.1%
594 1
 
0.1%
593 2
0.3%
592 1
 
0.1%
591 1
 
0.1%
589 3
0.4%
587 1
 
0.1%
586 1
 
0.1%

Screen On Time (hours/day)
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2727143
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:45.154862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12.5
median4.9
Q37.4
95-th percentile11.1
Maximum12
Range11
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation3.0685839
Coefficient of variation (CV)0.58197424
Kurtosis-0.81771677
Mean5.2727143
Median Absolute Deviation (MAD)2.4
Skewness0.45999193
Sum3690.9
Variance9.4162072
MonotonicityNot monotonic
2024-10-05T06:47:45.311360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 21
 
3.0%
2 17
 
2.4%
1.1 17
 
2.4%
1.3 15
 
2.1%
6.6 15
 
2.1%
1.7 14
 
2.0%
4 14
 
2.0%
1.2 14
 
2.0%
1.5 14
 
2.0%
1.4 12
 
1.7%
Other values (98) 547
78.1%
ValueCountFrequency (%)
1 3
 
0.4%
1.1 17
2.4%
1.2 14
2.0%
1.3 15
2.1%
1.4 12
1.7%
1.5 14
2.0%
1.6 21
3.0%
1.7 14
2.0%
1.8 9
1.3%
1.9 7
 
1.0%
ValueCountFrequency (%)
12 1
 
0.1%
11.9 3
0.4%
11.8 6
0.9%
11.7 2
 
0.3%
11.6 5
0.7%
11.5 2
 
0.3%
11.4 4
0.6%
11.3 4
0.6%
11.2 5
0.7%
11.1 4
0.6%

Battery Drain (mAh/day)
Real number (ℝ)

HIGH CORRELATION 

Distinct628
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1525.1586
Minimum302
Maximum2993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:45.448718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum302
5-th percentile378.8
Q1722.25
median1502.5
Q32229.5
95-th percentile2855.1
Maximum2993
Range2691
Interquartile range (IQR)1507.25

Descriptive statistics

Standard deviation819.13641
Coefficient of variation (CV)0.53708279
Kurtosis-1.2752753
Mean1525.1586
Median Absolute Deviation (MAD)764
Skewness0.13455857
Sum1067611
Variance670984.47
MonotonicityNot monotonic
2024-10-05T06:47:45.593194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
490 4
 
0.6%
590 3
 
0.4%
2911 3
 
0.4%
2447 3
 
0.4%
585 3
 
0.4%
558 3
 
0.4%
417 3
 
0.4%
639 2
 
0.3%
1483 2
 
0.3%
1641 2
 
0.3%
Other values (618) 672
96.0%
ValueCountFrequency (%)
302 1
0.1%
303 1
0.1%
308 1
0.1%
309 1
0.1%
310 1
0.1%
312 1
0.1%
313 1
0.1%
314 1
0.1%
315 1
0.1%
318 1
0.1%
ValueCountFrequency (%)
2993 1
0.1%
2984 1
0.1%
2971 1
0.1%
2968 2
0.3%
2956 1
0.1%
2954 1
0.1%
2953 1
0.1%
2947 1
0.1%
2936 1
0.1%
2932 1
0.1%

Number of Apps Installed
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.681429
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:45.737211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q126
median49
Q374
95-th percentile93
Maximum99
Range89
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.943324
Coefficient of variation (CV)0.53162124
Kurtosis-1.25492
Mean50.681429
Median Absolute Deviation (MAD)24
Skewness0.11173324
Sum35477
Variance725.94272
MonotonicityNot monotonic
2024-10-05T06:47:45.879899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16
 
2.3%
16 16
 
2.3%
18 15
 
2.1%
45 14
 
2.0%
13 14
 
2.0%
15 14
 
2.0%
19 13
 
1.9%
32 13
 
1.9%
90 13
 
1.9%
14 13
 
1.9%
Other values (76) 559
79.9%
ValueCountFrequency (%)
10 16
2.3%
11 12
1.7%
12 10
1.4%
13 14
2.0%
14 13
1.9%
15 14
2.0%
16 16
2.3%
17 13
1.9%
18 15
2.1%
19 13
1.9%
ValueCountFrequency (%)
99 9
1.3%
98 6
0.9%
97 6
0.9%
96 3
 
0.4%
95 4
 
0.6%
94 3
 
0.4%
93 5
 
0.7%
92 6
0.9%
91 12
1.7%
90 13
1.9%

Data Usage (MB/day)
Real number (ℝ)

HIGH CORRELATION 

Distinct585
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.74286
Minimum102
Maximum2497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:46.009329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile148.85
Q1373
median823.5
Q31341
95-th percentile2191.05
Maximum2497
Range2395
Interquartile range (IQR)968

Descriptive statistics

Standard deviation640.45173
Coefficient of variation (CV)0.68884824
Kurtosis-0.46038028
Mean929.74286
Median Absolute Deviation (MAD)476.5
Skewness0.69926441
Sum650820
Variance410178.42
MonotonicityNot monotonic
2024-10-05T06:47:46.144481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284 4
 
0.6%
122 4
 
0.6%
493 3
 
0.4%
313 3
 
0.4%
281 3
 
0.4%
596 3
 
0.4%
146 3
 
0.4%
265 3
 
0.4%
972 3
 
0.4%
208 3
 
0.4%
Other values (575) 668
95.4%
ValueCountFrequency (%)
102 1
0.1%
103 1
0.1%
105 2
0.3%
106 1
0.1%
107 1
0.1%
109 1
0.1%
111 1
0.1%
112 1
0.1%
113 2
0.3%
116 1
0.1%
ValueCountFrequency (%)
2497 1
0.1%
2493 1
0.1%
2481 2
0.3%
2479 1
0.1%
2477 1
0.1%
2471 1
0.1%
2460 1
0.1%
2450 1
0.1%
2441 2
0.3%
2438 2
0.3%

Age
Real number (ℝ)

Distinct42
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.482857
Minimum18
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-05T06:47:46.272712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q128
median38
Q349
95-th percentile57
Maximum59
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.012916
Coefficient of variation (CV)0.31216279
Kurtosis-1.2348589
Mean38.482857
Median Absolute Deviation (MAD)11
Skewness0.027974303
Sum26938
Variance144.31015
MonotonicityNot monotonic
2024-10-05T06:47:46.406128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
51 25
 
3.6%
34 25
 
3.6%
27 24
 
3.4%
22 24
 
3.4%
43 22
 
3.1%
55 21
 
3.0%
29 21
 
3.0%
25 21
 
3.0%
42 20
 
2.9%
31 20
 
2.9%
Other values (32) 477
68.1%
ValueCountFrequency (%)
18 11
1.6%
19 12
1.7%
20 17
2.4%
21 17
2.4%
22 24
3.4%
23 15
2.1%
24 14
2.0%
25 21
3.0%
26 14
2.0%
27 24
3.4%
ValueCountFrequency (%)
59 13
1.9%
58 14
2.0%
57 18
2.6%
56 16
2.3%
55 21
3.0%
54 16
2.3%
53 19
2.7%
52 17
2.4%
51 25
3.6%
50 15
2.1%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.0 KiB
Male
364 
Female
336 

Length

Max length6
Median length4
Mean length4.96
Min length4

Characters and Unicode

Total characters3472
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 364
52.0%
Female 336
48.0%

Length

2024-10-05T06:47:46.541613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T06:47:46.667151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 364
52.0%
female 336
48.0%

Most occurring characters

ValueCountFrequency (%)
e 1036
29.8%
a 700
20.2%
l 700
20.2%
M 364
 
10.5%
F 336
 
9.7%
m 336
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1036
29.8%
a 700
20.2%
l 700
20.2%
M 364
 
10.5%
F 336
 
9.7%
m 336
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1036
29.8%
a 700
20.2%
l 700
20.2%
M 364
 
10.5%
F 336
 
9.7%
m 336
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1036
29.8%
a 700
20.2%
l 700
20.2%
M 364
 
10.5%
F 336
 
9.7%
m 336
 
9.7%

User Behavior Class
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size34.3 KiB
2
146 
3
143 
4
139 
5
136 
1
136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Length

2024-10-05T06:47:46.770474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T06:47:46.879874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Most occurring characters

ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 146
20.9%
3 143
20.4%
4 139
19.9%
5 136
19.4%
1 136
19.4%

Interactions

2024-10-05T06:47:42.401921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:37.973644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.829562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.714770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.395892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.077225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.731814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.500093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.103002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.933591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.815881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.499929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.177764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.830688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.592915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.244172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.046647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.915425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.598580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.271172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.927768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.685293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.351639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.142335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.015575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.695348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.366882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.021017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.812683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.447169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.237754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.106933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.790471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.460712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.127843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.905141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.543229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.330587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.198180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.888262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.548054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.220825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.991065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:38.646435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:39.621342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.307429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:40.983082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:41.644383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T06:47:42.310795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-05T06:47:46.977039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeApp Usage Time (min/day)Battery Drain (mAh/day)Data Usage (MB/day)Device ModelGenderNumber of Apps InstalledOperating SystemScreen On Time (hours/day)User Behavior ClassUser ID
Age1.0000.005-0.006-0.0080.0000.0680.0000.0410.0190.0690.046
App Usage Time (min/day)0.0051.0000.9600.9600.0000.0270.9580.1020.9590.939-0.021
Battery Drain (mAh/day)-0.0060.9601.0000.9590.0000.0890.9600.0000.9590.892-0.020
Data Usage (MB/day)-0.0080.9600.9591.0000.0000.1360.9620.0800.9590.917-0.009
Device Model0.0000.0000.0000.0001.0000.0430.0000.9980.0000.0000.072
Gender0.0680.0270.0890.1360.0431.0000.0270.0000.0440.0650.000
Number of Apps Installed0.0000.9580.9600.9620.0000.0271.0000.0000.9580.910-0.021
Operating System0.0410.1020.0000.0800.9980.0000.0001.0000.0000.0000.072
Screen On Time (hours/day)0.0190.9590.9590.9590.0000.0440.9580.0001.0000.885-0.012
User Behavior Class0.0690.9390.8920.9170.0000.0650.9100.0000.8851.0000.000
User ID0.046-0.021-0.020-0.0090.0720.000-0.0210.072-0.0120.0001.000

Missing values

2024-10-05T06:47:43.116916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-05T06:47:43.357414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

User IDDevice ModelOperating SystemApp Usage Time (min/day)Screen On Time (hours/day)Battery Drain (mAh/day)Number of Apps InstalledData Usage (MB/day)AgeGenderUser Behavior Class
01Google Pixel 5Android3936.4187267112240Male4
12OnePlus 9Android2684.713314294447Female3
23Xiaomi Mi 11Android1544.07613232242Male2
34Google Pixel 5Android2394.816765687120Male3
45iPhone 12iOS1874.313675898831Female3
56Google Pixel 5Android992.09403556431Male2
67Samsung Galaxy S21Android3507.3180266105421Female4
78OnePlus 9Android54311.4295682170231Male5
89Samsung Galaxy S21Android3407.7213875105342Female4
910iPhone 12iOS4246.6195775130142Male4
User IDDevice ModelOperating SystemApp Usage Time (min/day)Screen On Time (hours/day)Battery Drain (mAh/day)Number of Apps InstalledData Usage (MB/day)AgeGenderUser Behavior Class
690691Google Pixel 5Android1955.714474867930Male3
691692iPhone 12iOS1784.08563756951Female2
692693Xiaomi Mi 11Android3786.7189878145548Female4
693694Xiaomi Mi 11Android5058.6279282170931Male5
694695Samsung Galaxy S21Android5649.7242283198534Female5
695696iPhone 12iOS923.910822638122Male2
696697Xiaomi Mi 11Android3166.8196568120159Male4
697698Google Pixel 5Android993.19422245750Female2
698699Samsung Galaxy S21Android621.74311322444Male1
699700OnePlus 9Android2125.413064982823Female3